class Perceptron extends PredictorMat
The Perceptron class supports single-output, 2-layer (input and output)
Neural-Networks. Although perceptrons are typically used for classification,
this class is used for prediction. Given several input vectors and output
values (training data), fit the weights/parameters 'b' connecting the layers,
so that for a new input vector 'z', the net can predict the output value, i.e.,
z = f (b dot z)
The parameter vector 'b' (w) gives the weights between input and output layers. Note, 'b0' is treated as the bias, so 'x0' must be 1.0.
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- PredictorMat
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Instance Constructors
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new
Perceptron(x: MatriD, y: VectoD, fname_: Strings = null, hparam: HyperParameter = Optimizer.hp, f1: AFF = f_sigmoid)
- x
the input m-by-n matrix (training data consisting of m input vectors)
- y
the output m-vector (training data consisting of m output values)
- fname_
the feature/variable names
- hparam
the hyper-parameters
- f1
the activation function family for layers 1->2 (input to output)
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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val
b: VectoD
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- Definition Classes
- Predictor
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def
clone(): AnyRef
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def
crossVal(k: Int = 10, rando: Boolean = true): Unit
Perform 'k'-fold cross-validation.
Perform 'k'-fold cross-validation.
- k
the number of folds
- rando
whether to use randomized cross-validation
- Definition Classes
- Perceptron → PredictorMat
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def
crossValidate(algor: (MatriD, VectoD) ⇒ PredictorMat, k: Int = 10, rando: Boolean = true): Array[Statistic]
- Definition Classes
- PredictorMat
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def
diagnose(e: VectoD, w: VectoD = null, yp: VectoD = null, y_: VectoD = y): Unit
Given the error/residual vector, compute the quality of fit measures.
Given the error/residual vector, compute the quality of fit measures.
- e
the corresponding m-dimensional error vector (y - yp)
- w
the weights on the instances
- yp
the predicted response vector (x * b)
- Definition Classes
- Fit
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val
e: VectoD
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- protected
- Definition Classes
- Predictor
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final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
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def
equals(arg0: Any): Boolean
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def
eval(): Unit
Evaluate the quality of fit for the current parameter/weight vector 'b'.
Evaluate the quality of fit for the current parameter/weight vector 'b'.
- Definition Classes
- Perceptron → PredictorMat → Predictor
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def
eval(xx: MatriD, yy: VectoD): Unit
Compute the error and useful diagnostics for the test dataset.
Compute the error and useful diagnostics for the test dataset.
- xx
the test data matrix
- yy
the test response vector
- Definition Classes
- PredictorMat → Predictor
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def
finalize(): Unit
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def
fit: VectoD
Return the quality of fit including 'rSq', 'sst', 'sse', 'mse0', rmse', 'mae', 'df._2', 'rBarSq', 'fStat', 'aic', 'bic'.
Return the quality of fit including 'rSq', 'sst', 'sse', 'mse0', rmse', 'mae', 'df._2', 'rBarSq', 'fStat', 'aic', 'bic'. Note, if 'sse > sst', the model introduces errors and the 'rSq' may be negative, otherwise, R^2 ('rSq') ranges from 0 (weak) to 1 (strong). Note that 'rSq' is the number 5 measure. Override to add more quality of fit measures.
- Definition Classes
- Fit
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def
fitLabel: Seq[String]
Return the labels for the quality of fit measures.
Return the labels for the quality of fit measures. Override to add more quality of fit measures.
- Definition Classes
- Fit
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def
fitMap: Map[String, String]
Build a map of quality of fit measures (use of
LinedHashMapmakes it ordered).Build a map of quality of fit measures (use of
LinedHashMapmakes it ordered). Override to add more quality of fit measures.- Definition Classes
- Fit
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final
def
flaw(method: String, message: String): Unit
- Definition Classes
- Error
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var
fname: Strings
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- PredictorMat
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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def
hparameter: HyperParameter
Return the hyper-parameters.
Return the hyper-parameters.
- Definition Classes
- PredictorMat
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val
index_rSq: Int
- Definition Classes
- Fit
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
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val
k: Int
- Attributes
- protected
- Definition Classes
- PredictorMat
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val
m: Int
- Attributes
- protected
- Definition Classes
- PredictorMat
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def
mse_: Double
Return the mean of squares for error (sse / df._2).
Return the mean of squares for error (sse / df._2). Must call diagnose first.
- Definition Classes
- Fit
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final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
parameter: VectoD
Return the vector of parameter/coefficient values.
Return the vector of parameter/coefficient values.
- Definition Classes
- Predictor
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def
predict(z: MatriD = x): VectoD
Given a new input matrix 'z', predict the output/response value 'f(z)'.
Given a new input matrix 'z', predict the output/response value 'f(z)'.
- z
the new input matrix
- Definition Classes
- Perceptron → PredictorMat
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def
predict(z: VectoD): Double
Given a new input vector 'z', predict the output/response value 'f(z)'.
Given a new input vector 'z', predict the output/response value 'f(z)'.
- z
the new input vector
- Definition Classes
- Perceptron → PredictorMat → Predictor
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def
predict(z: VectoI): Double
Given a new discrete data vector z, predict the y-value of f(z).
Given a new discrete data vector z, predict the y-value of f(z).
- z
the vector to use for prediction
- Definition Classes
- Predictor
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def
reset(eta_: Double): Unit
Reset the learning rate 'eta'.
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def
resetDF(df_update: (Double, Double)): Unit
Reset the degrees of freedom to the new updated values.
Reset the degrees of freedom to the new updated values. For some models, the degrees of freedom is not known until after the model is built.
- df_update
the updated degrees of freedom
- Definition Classes
- Fit
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def
residual: VectoD
Return the vector of residuals/errors.
Return the vector of residuals/errors.
- Definition Classes
- Predictor
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def
setWeights(w0: VectoD): Unit
Set the initial parameter/weight vector 'b' manually before training.
Set the initial parameter/weight vector 'b' manually before training. This is mainly for testing purposes.
- w0
the initial weights for b
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def
sumCoeff(b: VectoD, stdErr: VectoD = null): String
Produce the summary report portion for the cofficients.
Produce the summary report portion for the cofficients.
- b
the parameters/coefficients for the model
- Definition Classes
- Fit
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def
summary(): String
Compute and return summary diagostics for the regression model.
Compute and return summary diagostics for the regression model.
- Definition Classes
- PredictorMat
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def
summary(b: VectoD, stdErr: VectoD = null, show: Boolean = false): String
Produce a summary report with diagnostics for each predictor 'x_j' and the overall quality of fit.
Produce a summary report with diagnostics for each predictor 'x_j' and the overall quality of fit.
- b
the parameters/coefficients for the model
- show
flag indicating whether to print the summary
- Definition Classes
- Fit
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
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def
toString(): String
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def
train(yy: VectoD = y): Perceptron
Given training data 'x' and 'yy', fit the parameter/weight vector 'b'.
Given training data 'x' and 'yy', fit the parameter/weight vector 'b'. Minimize the error in the prediction by adjusting the weight vector 'b'. Iterate over several epochs, where each epoch divides the training set into 'nbat' batches. Each batch is used to update the weights.
- yy
the output vector
- Definition Classes
- Perceptron → PredictorMat → Predictor
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def
train(): PredictorMat
Given a set of data vectors 'x's and their corresponding responses 'y's, passed into the implementing class, train the prediction function 'y = f(x)' by fitting its parameters.
Given a set of data vectors 'x's and their corresponding responses 'y's, passed into the implementing class, train the prediction function 'y = f(x)' by fitting its parameters.
- Definition Classes
- PredictorMat
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def
train0(yy: VectoD = y): Perceptron
Given training data 'x' and 'yy', fit the parameter/weight vector 'b'.
Given training data 'x' and 'yy', fit the parameter/weight vector 'b'. Minimize the error in the prediction by adjusting the weight vector 'b'. The error 'e' is simply the difference between the target value 'yy' and the predicted value 'yp'. Minimize the dot product of error with itself using gradient-descent (move in the opposite direction of the gradient). Iterate over several epochs (no batching).
- yy
the output vector
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def
train2(yy: VectoD = y): Perceptron
Given training data 'x' and 'yy', fit the parameter/weight vector 'b'.
Given training data 'x' and 'yy', fit the parameter/weight vector 'b'. Minimize the error in the prediction by adjusting the weight vector 'b'. Iterate over several epochs, where each epoch divides the training set into 'nbat' batches. Each batch is used to update the weights. This version preforms an interval search for the best 'eta' value.
- yy
the output vector
- Definition Classes
- Perceptron → PredictorMat
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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val
x: MatriD
- Attributes
- protected
- Definition Classes
- PredictorMat
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val
y: VectoD
- Attributes
- protected
- Definition Classes
- PredictorMat